OpenRaiser/Intern-Atlas huggingface dataset:
A Directional Diffusion Graph Transformer for Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

In real-world recommender systems, implicitly collected user feedback, while abundant, often includes noisy false-positive and false-negative interactions. The possible misinterpretations of the user-item interactions pose a significant challenge for traditional graph neural recommenders. These approaches aggregate the users' or items' neighbours based on implicit user-item interactions in order to accurately capture the users' profiles. To account for and model possible noise in the users' interactions in graph neural recommenders, we propose a novel Diffusion Graph Transformer (DiffGT) model for top-k recommendation. Our DiffGT model employs a diffusion process, which includes a forward phase for gradually introducing noise to implicit interactions, followed by a reverse process to iteratively refine the representations of the users' hidden preferences (i.e., a denoising process). In our proposed approach, given the inherent anisotropic structure observed in the user-item interaction graph, we specifically use anisotropic and directional Gaussian noises in the forward diffusion process. Our approach differs from the sole use of isotropic Gaussian noises in existing diffusion models. In the reverse diffusion process, to reverse the effect of noise added earlier and recover the true users' preferences, we integrate a graph transformer architecture with a linear attention module to denoise the noisy user/item embeddings in an effective and efficient manner. In addition, such a reverse diffusion process is further guided by personalised information (e.g., interacted items) to enable the accurate estimation of the users' preferences on items. Our extensive experiments conclusively demonstrate the superiority of our proposed graph diffusion model over ten existing state-of-the-art approaches across three benchmark datasets.
Influence of Gameplay Duration, Hand Tracking, and Controller Based Control Methods on UX in VR
Venue: arXiv cs.HC 2024
Year: 2024

Inside-out tracking is growing popular in consumer VR, enhancing accessibility. It uses HMD camera data and neural networks for effective hand tracking. However, limited user experience studies have compared this method to traditional controllers, with no consensus on the optimal control technique. This paper investigates the impact of control methods and gaming duration on VR user experience, hypothesizing hand tracking might be preferred for short sessions and by users new to VR due to its simplicity. Through a lab study with twenty participants, evaluating presence, emotional response, UX quality, and flow, findings revealed control type and session length affect user experience without significant interaction. Controllers were generally superior, attributed to their reliability, and longer sessions increased presence and realism. The study found that individuals with more VR experience were more inclined to recommend hand tracking to others, which contradicted predictions.
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations
Venue: arXiv cs.LG 2024
Year: 2024

In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transparency and trustworthiness in ML models. XAI techniques aim to enhance the transparency of ML models by providing insights, in terms of model's explanations, into their decision-making process. Simultaneously, some MLaaS platforms now offer explanations alongside the ML prediction outputs. This setup has elevated concerns regarding vulnerabilities in MLaaS, particularly in relation to privacy leakage attacks such as model extraction attacks (MEA). This is due to the fact that explanations can unveil insights about the inner workings of the model which could be exploited by malicious users. In this work, we focus on investigating how model explanations, particularly counterfactual explanations (CFs), can be exploited for performing MEA within the MLaaS platform. We also delve into assessing the effectiveness of incorporating differential privacy (DP) as a mitigation strategy. To this end, we first propose a novel approach for MEA based on Knowledge Distillation (KD) to enhance the efficiency of extracting a substitute model of a target model exploiting CFs, without any knowledge about the training data distribution by the attacker. Then, we advise an approach for training CF generators incorporating DP to generate private CFs. We conduct thorough experimental evaluations on real-world datasets and demonstrate that our proposed KD-based MEA can yield a high-fidelity substitute model with a reduced number of queries with respect to baseline approaches. Furthermore, our findings reveal that including a privacy layer can allow mitigating the MEA. However, on the account of the quality of CFs, impacts the performance of the explanations.
DIDA: Denoised Imitation Learning based on Domain Adaptation
Venue: arXiv cs.LG 2024
Year: 2024

Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or transmission. Previous IL methods improve the robustness of learned policies by injecting an adversarially learned Gaussian noise into pure expert data or utilizing additional ranking information, but they may fail in the LND setting. To alleviate the above problems, we propose Denoised Imitation learning based on Domain Adaptation (DIDA), which designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations. Experiment results on MuJoCo demonstrate that DIDA can successfully handle challenging imitation tasks from demonstrations with various types of noise, outperforming most baseline methods.
Heckler: Breaking Confidential VMs with Malicious Interrupts
Venue: arXiv cs.CR 2024
Year: 2024

Hardware-based Trusted execution environments (TEEs) offer an isolation granularity of virtual machine abstraction. They provide confidential VMs (CVMs) that host security-sensitive code and data. AMD SEV-SNP and Intel TDX enable CVMs and are now available on popular cloud platforms. The untrusted hypervisor in these settings is in control of several resource management and configuration tasks, including interrupts. We present Heckler, a new attack wherein the hypervisor injects malicious non-timer interrupts to break the confidentiality and integrity of CVMs. Our insight is to use the interrupt handlers that have global effects, such that we can manipulate a CVM's register states to change the data and control flow. With AMD SEV-SNP and Intel TDX, we demonstrate Heckler on OpenSSH and sudo to bypass authentication. On AMD SEV-SNP we break execution integrity of C, Java, and Julia applications that perform statistical and text analysis. We explain the gaps in current defenses and outline guidelines for future defenses.
Boosting Unsupervised Segmentation Learning
Venue: arXiv cs.CV 2024
Year: 2024

We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoiding substantial computational costs. Secondly, we introduce a multi-scale consistency criterion, based on a teacher-student training scheme. This criterion matches segmentation masks predicted from regions of the input image extracted at different resolutions to each other. Experimental results on several benchmarks used in unsupervised segmentation learning demonstrate the effectiveness of our proposed techniques.
Movable Antennas-Assisted Secure Transmission Without Eavesdroppers' Instantaneous CSI
Venue: arXiv cs.IT 2024
Year: 2024

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice.As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.
Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks
Venue: arXiv cs.RO 2024
Year: 2024

Automating long-horizon tasks with a robotic arm has been a central research topic in robotics. Optimization-based action planning is an efficient approach for creating an action plan to complete a given task. Construction of a reliable planning method requires a design process of conditions, e.g., to avoid collision between objects. The design process, however, has two critical issues: 1) iterative trials--the design process is time-consuming due to the trial-and-error process of modifying conditions, and 2) manual redesign--it is difficult to cover all the necessary conditions manually. To tackle these issues, this paper proposes a future-predictive success-or-failure-classification method to obtain conditions automatically. The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions. The proposed method uses a long-horizon future-prediction method to enable success-or-failure classification without the execution of an action plan. This paper also proposes a regularization term called transition consistency regularization to provide easy-to-predict feature distribution. The regularization term improves future prediction and classification performance. The effectiveness of our method is demonstrated through classification and robotic-manipulation experiments.
Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions
Venue: arXiv cs.CL 2024
Year: 2024

Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language models (LLMs) further entitle the systems to complex and coherent conversational interactions. These systems would be of great help in language education as it involves developing skills in communication, which, however, drew relatively less attention. Additionally, due to the complicated cognitive development at younger ages, more endeavors are needed for practical uses. Scaffolding refers to a teaching technique where teachers provide support and guidance to students for learning and developing new concepts or skills. It is an effective way to support diverse learning needs, goals, processes, and outcomes. In this work, we investigate how pedagogical instructions facilitate the scaffolding in ITSs, by conducting a case study on guiding children to describe images for language learning. We construct different types of scaffolding tutoring systems grounded in four fundamental learning theories: knowledge construction, inquiry-based learning, dialogic teaching, and zone of proximal development. For qualitative and quantitative analyses, we build and refine a seven-dimension rubric to evaluate the scaffolding process. In our experiment on GPT-4V, we observe that LLMs demonstrate strong potential to follow pedagogical instructions and achieve self-paced learning in different student groups. Moreover, we extend our evaluation framework from a manual to an automated approach, paving the way to benchmark various conversational tutoring systems.
MEDIATE: Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange
Venue: arXiv cs.MA 2024
Year: 2024

Recent advances in multi-agent systems (MAS) have shown that incorporating peer incentivization (PI) mechanisms vastly improves cooperation. Especially in social dilemmas, communication between the agents helps to overcome sub-optimal Nash equilibria. However, incentivization tokens need to be carefully selected. Furthermore, real-world applications might yield increased privacy requirements and limited exchange. Therefore, we extend the PI protocol for mutual acknowledgment token exchange (MATE) and provide additional analysis on the impact of the chosen tokens. Building upon those insights, we propose mutually endorsed distributed incentive acknowledgment token exchange (MEDIATE), an extended PI architecture employing automatic token derivation via decentralized consensus. Empirical results show the stable agreement on appropriate tokens yielding superior performance compared to static tokens and state-of-the-art approaches in different social dilemma environments with various reward distributions.
Synergy as the failure of distributivity
Venue: arXiv cs.IT 2024
Year: 2024

The concept of emergence, or synergy in its simplest form, is widely used but lacks a rigorous definition. Our work connects information and set theory to uncover the mathematical nature of synergy as the failure of distributivity. It resolves the persistent self-contradiction of information decomposition theory and reinstates it as a primary route toward a rigorous definition of emergence. Our results suggest that non-distributive variants of set theory may be used to describe emergent physical systems.
Generative AI and Teachers -- For Us or Against Us? A Case Study
Venue: arXiv cs.CL 2024
Year: 2024

We present insightful results of a survey on the adoption of generative artificial intelligence (GenAI) by university teachers in their teaching activities. The transformation of education by GenAI, particularly large language models (LLMs), has been presenting both opportunities and challenges, including cheating by students. We prepared the online survey according to best practices and the questions were created by the authors, who have pedagogy experience. The survey contained 12 questions and a pilot study was first conducted. The survey was then sent to all teachers in multiple departments across different campuses of the university of interest in Sweden: Luleå University of Technology. The survey was available in both Swedish and English. The results show that 35 teachers (more than half) use GenAI out of 67 respondents. Preparation is the teaching activity with the most frequency that GenAI is used for and ChatGPT is the most commonly used GenAI. 59% say it has impacted their teaching, however, 55% say there should be legislation around the use of GenAI, especially as inaccuracies and cheating are the biggest concerns.
Streamlining CXL Adoption for Hyperscale Efficiency
Venue: arXiv cs.ET 2024
Year: 2024

In our exploration of Composable Memory systems utilizing CXL, we focus on overcoming adoption barriers at Hyperscale, underscored by economic models demonstrating Total Cost of Ownership (TCO). While CXL addresses the pressing memory capacity needs of emerging Hyperscale applications, the escalating demands from evolving use cases such as AI outpace the capabilities of current CXL solutions. Hyperscalers resort to software-based memory (de)compression technology, alleviating memory capacity, storage, and network constraints but incurring a notable "Tax" on Compute CPU cycles. As a pivotal guide to the CXL community, Hyperscalers have formulated the groundbreaking Open Compute Project (OCP) Hyperscale CXL Tiered Memory Expander specification. If implemented, this specification lowers TCO adoption barriers, enabling diverse CXL deployments at both Hyperscaler and Enterprise levels. We present a CXL integrated solution, aligning with the aforementioned specification, introducing an energy-efficient, scalable, hardware-accelerated, Lossless Compressed Memory CXL Tier. This solution, slated for mid-2024 production and open for integration with Memory Expander controller manufacturers, offers 2-3X CXL memory compression in nanoseconds, delivering a 20-25% reduction in TCO for end customers without requiring additional physical slots. In our discussion, we pinpoint areas for collaborative innovation within the CXL Community to expedite software/hardware advancements for CXL Tiered Memory Expansion. Furthermore, we delve into unresolved challenges in Pooled deployment and explore potential solutions, collectively aiming to make CXL adoption a "No Brainer" at Hyperscale.
Bringing memory to Boolean networks: a unifying framework
Venue: arXiv cs.LO 2024
Year: 2024

Boolean networks are extensively applied as models of complex dynamical systems, aiming at capturing essential features related to causality and synchronicity of the state changes of components along time. Dynamics of Boolean networks result from the application of their Boolean map according to a so-called update mode, specifying the possible transitions between network configurations. In this paper, we explore update modes that possess a memory on past configurations, and provide a generic framework to define them. We show that recently introduced modes such as the most permissive and interval modes can be naturally expressed in this framework. We propose novel update modes, the history-based and trapping modes, and provide a comprehensive comparison between them. Furthermore, we show that trapping dynamics, which further generalize the most permissive mode, correspond to a rich class of networks related to transitive dynamics and encompassing commutative networks. Finally, we provide a thorough characterization of the structure of minimal and principal trapspaces, bringing a combinatorial and algebraic understanding of these objects.
Towards Trustworthy Automated Program Verifiers: Formally Validating Translations into an Intermediate Verification Language (extended version)
Venue: arXiv cs.PL 2024
Year: 2024

Automated program verifiers are typically implemented using an intermediate verification language (IVL), such as Boogie or Why3. A verifier front-end translates the input program and specification into an IVL program, while the back-end generates proof obligations for the IVL program and employs an SMT solver to discharge them. Soundness of such verifiers therefore requires that the front-end translation faithfully captures the semantics of the input program and specification in the IVL program, and that the back-end reports success only if the IVL program is actually correct. For a verification tool to be trustworthy, these soundness conditions must be satisfied by its actual implementation, not just the program logic it uses. In this paper, we present a novel validation methodology that, given a formal semantics for the input language and IVL, provides formal soundness guarantees for front-end implementations. For each run of the verifier, we automatically generate a proof in Isabelle showing that the correctness of the produced IVL program implies the correctness of the input program. This proof can be checked independently from the verifier, in Isabelle, and can be combined with existing work on validating back-ends to obtain an end-to-end soundness result. Our methodology based on forward simulation employs several modularisation strategies to handle the large semantic gap between the input language and the IVL, as well as the intricacies of practical, optimised translations. We present our methodology for the widely-used Viper and Boogie languages. Our evaluation shows that it is effective in validating the translations performed by the existing Viper implementation.
X-lifecycle Learning for Cloud Incident Management using LLMs
Venue: arXiv cs.NI 2024
Year: 2024

Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development lifecycle [SDLC] (e.g., codes, configuration, monitor data, service properties, service dependencies, trouble-shooting documents, etc.) to generate insights for detection, root causing and mitigating of incidents. Recent advancements in large language models [LLMs] (e.g., ChatGPT, GPT-4, Gemini) created opportunities to automatically generate contextual recommendations to the OCEs assisting them to quickly identify and mitigate critical issues. However, existing research typically takes a silo-ed view for solving a certain task in incident management by leveraging data from a single stage of SDLC. In this paper, we demonstrate that augmenting additional contextual data from different stages of SDLC improves the performance of two critically important and practically challenging tasks: (1) automatically generating root cause recommendations for dependency failure related incidents, and (2) identifying ontology of service monitors used for automatically detecting incidents. By leveraging 353 incident and 260 monitor dataset from Microsoft, we demonstrate that augmenting contextual information from different stages of the SDLC improves the performance over State-of-The-Art methods.
Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying
Venue: arXiv cs.LG 2024
Year: 2024

In today's digital world, cyberbullying is a serious problem that can harm the mental and physical health of people who use social media. This paper explains just how serious cyberbullying is and how it really affects indi-viduals exposed to it. It also stresses how important it is to find better ways to detect cyberbullying so that online spaces can be safer. Plus, it talks about how making more accurate tools to spot cyberbullying will be really helpful in the future. Our paper introduces a deep learning-based ap-proach, primarily employing BERT and BiLSTM architectures, to effective-ly address cyberbullying. This approach is designed to analyse large vol-umes of posts and predict potential instances of cyberbullying in online spaces. Our results demonstrate the superiority of the hateBERT model, an extension of BERT focused on hate speech detection, among the five mod-els, achieving an accuracy rate of 89.16%. This research is a significant con-tribution to "Computational Intelligence for Social Transformation," prom-ising a safer and more inclusive digital landscape.